Zero-Shot Image Classification
Transformers
PyTorch
Safetensors
vision-text-dual-encoder
image generation
visual qa
text-image embedding
image-text embedding
sartify
visual conversional ai
image semantic retrival
african raw resourced languages
Instructions to use sartifyllc/AViLaMa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sartifyllc/AViLaMa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sartifyllc/AViLaMa") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("sartifyllc/AViLaMa") model = AutoModel.from_pretrained("sartifyllc/AViLaMa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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**BibTeX:**
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```bibtex
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@inproceedings{sartifyllc2023,
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title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
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author={Innocent Charles and
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Zephania Reuben},
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booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
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year={2023},
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url={https://sartify.com}
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}
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AViLaMa paper
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```
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@article{sartifyllc2023africanvision,
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title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
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author={Innocent Charles and
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**BibTeX:**
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```bibtex
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AViLaMa paper
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@article{sartifyllc2023africanvision,
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title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
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author={Innocent Charles and
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